A Novel Quantum Entanglement-Inspired Meta-heuristic Framework for Solving Multimodal Optimization Problems

To solve Multimodal optimization problems (MOPs), a Novel Quantum entanglement-inspired meta-heuristic framework (NMF-QE) is proposed. Its main inspirations are two concepts of quantum physics: quantum entanglement and quantum superposition. When given Proto-born particles (PBPs) of a population, these two concepts are mathematically developed to generate twin-born and combination-born particles, respectively. And if any elite-born particles would be created by a local re-searching strategy. These three or four groups of particles come together as a whole search population of NMF-QE to realize exploration and exploitation of algorithms. To guarantee dynamical optimization capability of NMF-QE, the individual evolutionary mechanism of some existing meta-heuristics will be adopted to iteratively create PBPs. A selected meta-heuristic is coupled with NMF-QE to present its improved variant. Numerical results show that the proposed NMF-QE can effectively improve optimization performance of meta-heuristics on MOPs.